Join us for 3 days packed with panels, workshops, hackathons, talks and more! If you’re an AI enthusiast, you cannot miss this event!
The second edition of IndabaX Tunisia will take place from the 8th to the 10th of october 2021 as a hybrid event.
It is an opportunity for the Tunisian AI community to meet, discover the most recent advances in the field and interact with world-leading AI researchers.
If you are a beginner and curious to learn more or if you are an expert in Machine Learning, you are encouraged to join us and be part of a network fully committed to building the future of AI in Tunisia.
In partnership with ZINDI, we have prepared for you two hackathons with different levels of difficulty to suit your interests.
The aim of this competition is to introduce newcomers to the field of AI and to help them set their stepping stone in the world of Machine Learning. This Competition will be guided through Starter-Notebooks and our team’s assistance. If this is your first time participating in a ZINDI hackathon, this is the one for you.
This competition will be a step higher in the level of difficulty and will target a real-life problem which will be announced on the day of the competition. An extremely significant prize pool awaits the winners. If you have the competitive spirit and you’re looking for a win to prove your skills, this is your go to choice. You can participate in teams of two to three members or individually.
By Karim Beguir
By Amanda Minnich
By Tristan CAZENAVE
By Kaouthar Boussema
By Dr Sinda Ben Salem
With the increasing availability of large-scale biological data, the
applications of deep learning approaches are now expanding to several fields of
healthcare and life sciences. The promise of tackling problems that could
benefit the health of millions of people together with the necessity to analyse
highly dimensional and highly structured data, have led to key developments in
machine learning applied to diagnostic imaging, disease severity prediction and
proteomics. As proteins are the machinery of life, one of the crucial challenges
for computational biology is to be able to predict the function and 3D structure
of proteins from their sequences of amino-acids alone. Recently, such
predictions have been made possible by the use of language modeling
self-supervised trainings and the use of state-of-the-art architecture like the
transformer, to help closing the gap of sequence annotation. In this workshop,
we will show participants how to use protein pre-trained embeddings to build a
classifier of African Covid-19 strains and predict their respective 3D
structures for comparison. We will provide the necessary guidance, tools and
background to understand the key concepts of biology, how to use protein
pre-trained embeddings and how to predict protein 3D structures from scratch
At the end of the workshop, participants should have a good understanding of how
to use pre-trained embeddings and de novo structure predictions to help protein
characterisation as well as knowledge about real-life use-cases. On the
practical side, participants:
Should understand the differences between several protein embeddings and be able
to compute them for any protein sequence.
Are able to perform protein 3D structure predictions and compare them with the
Participants should be comfortable with programming in Python.
Have a basic understanding of key concepts in machine learning and in
particular, natural language processing.
No prior knowledge of biology is expected or required.
By Marie Lopez, Mohamed Mounir Moussa , Ibtissem Kadri
Qwiklabs Workshop Description
Build a simple End-to-End Machine Learning solution for Predicting Housing
Prices using Tensorflow and AI Platform and leverage the Cloud for distributed
training and online prediction.
By Mouafek Ayadi
Learn the basics of assessing the security of your ML modals with Azure/counterfit:
a CLI that provides a generic automation layer for assessing the security of ML models.
Check the project on github for more information:
By Amanda Minnich & Will Pearce
Learn the basics of building a PyTorch model using a structured, incremental and from first
principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and
how to make use of its capabilities: autograd, dynamic computation graph, model classes,
data loaders and more.
The main goal of this session is to show you how PyTorch works: we will start with a simple
and familiar example in Numpy and "torch" it! At the end of it, you should be able to
understand PyTorch's key components and how to assemble them together into a working model.
We will use Google Colab and work our way together into building a complete model in
PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object
By Daniel Godoy